Risk factors selection in automobile insurance policies: a way to improve the bottom line of insurance companies
Autor(a) principal: | |
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Data de Publicação: | 2015 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng por |
Título da fonte: | Revista Brasileira de Gestão de Negócios (Online) |
Texto Completo: | https://rbgn.fecap.br/RBGN/article/view/1741 |
Resumo: | Objective – The objective of this paper is to test the validity of using ‘bonus-malus’ (BM) levels to classify policyholders satisfactorily.Design/methodology/approach – In order to achieve the proposed objective and to show empirical evidence, an artificial intelligence method, Rough Set theory, has been employed.Findings – The empirical evidence shows that common risk factors employed by insurance companies are good explanatory variables for classifying car policyholders’ policies. In addition, the BM level variable slightly increases the explanatory power of the a priori risks factors.Practical implications – To increase the prediction capacity of BM level, psychological questionnaires could be used to measure policyholders’ hidden characteristics.Contributions – The main contribution is that the methodology used to carry out research, the Rough Set Theory, has not been applied to this problem. |
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Risk factors selection in automobile insurance policies: a way to improve the bottom line of insurance companiesSelección de los factores de riesgo en las políticas de seguros de automóvil: una forma de mejorar las ganancias de las compañías de segurosSeleção dos fatores de risco nas políticas de seguro de automóveis: uma maneira de aprimorar os lucros das companhias de seguroautomobile insurance companyrisk factorsbonus malus systemrough set theoryartificial intelligence.JELC20G22companhia de seguros automobilísticosfatores de riscosistema de “bonus- malus”teoria de Rough Setinteligência artificial.Objective – The objective of this paper is to test the validity of using ‘bonus-malus’ (BM) levels to classify policyholders satisfactorily.Design/methodology/approach – In order to achieve the proposed objective and to show empirical evidence, an artificial intelligence method, Rough Set theory, has been employed.Findings – The empirical evidence shows that common risk factors employed by insurance companies are good explanatory variables for classifying car policyholders’ policies. In addition, the BM level variable slightly increases the explanatory power of the a priori risks factors.Practical implications – To increase the prediction capacity of BM level, psychological questionnaires could be used to measure policyholders’ hidden characteristics.Contributions – The main contribution is that the methodology used to carry out research, the Rough Set Theory, has not been applied to this problem. Objetivo – o objetivo deste trabalho é testar a validade do uso de níveis “bonus-malus” (BM) para classificar satisfatoriamente os segurados.Método – A fim de alcançar o objetivo proposto e mostrar a evidência empírica, um método de inteligência artificial, a teoria de Rough Set, foi aplicado.Resultados – A evidência empírica mostra que os fatores de risco comuns empregados pela companhia de seguros são boas variáveis explicativas para classificar políticas dos segurados. Além disso, a variável do nível de BM aumenta ligeiramente o poder explicativo dos fatores de risco a priori.Implicações práticas – Para aumentar a capacidade de previsão do nível de BM, questionários psicológicos poderiam ser usados para medir as características ocultas dos segurados.Contribuições – A principal contribuição é que a metodologia utilizada para realizar a pesquisa, teoria de Rough Set, não foi ainda aplicada a esse problema. FECAP2015-12-16info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionAvaliado por paresapplication/pdfapplication/pdfhttps://rbgn.fecap.br/RBGN/article/view/174110.7819/rbgn.v17i57.1741Review of Business Management; Vol. 17 No. 57 (2015); 1228-1245RBGN Revista Brasileira de Gestão de Negócios; Vol. 17 Núm. 57 (2015); 1228-1245RBGN - Revista Brasileira de Gestão de Negócios; v. 17 n. 57 (2015); 1228-12451983-08071806-4892reponame:Revista Brasileira de Gestão de Negócios (Online)instname:Fundação Escola de Comércio Álvares Penteado (FECAP)instacron:FECAPengporhttps://rbgn.fecap.br/RBGN/article/view/1741/pdfhttps://rbgn.fecap.br/RBGN/article/view/1741/pdf_1Segovia-Vargas, María-JesúsCamacho-Miñano, María-del-MarPascual-Ezama, Davidinfo:eu-repo/semantics/openAccess2021-07-21T16:27:41Zoai:ojs.emnuvens.com.br:article/1741Revistahttp://rbgn.fecap.br/RBGN/indexhttps://rbgn.fecap.br/RBGN/oai||jmauricio@fecap.br1983-08071806-4892opendoar:2021-07-21T16:27:41Revista Brasileira de Gestão de Negócios (Online) - Fundação Escola de Comércio Álvares Penteado (FECAP)false |
dc.title.none.fl_str_mv |
Risk factors selection in automobile insurance policies: a way to improve the bottom line of insurance companies Selección de los factores de riesgo en las políticas de seguros de automóvil: una forma de mejorar las ganancias de las compañías de seguros Seleção dos fatores de risco nas políticas de seguro de automóveis: uma maneira de aprimorar os lucros das companhias de seguro |
title |
Risk factors selection in automobile insurance policies: a way to improve the bottom line of insurance companies |
spellingShingle |
Risk factors selection in automobile insurance policies: a way to improve the bottom line of insurance companies Segovia-Vargas, María-Jesús automobile insurance company risk factors bonus malus system rough set theory artificial intelligence. JEL C20 G22 companhia de seguros automobilísticos fatores de risco sistema de “bonus- malus” teoria de Rough Set inteligência artificial. |
title_short |
Risk factors selection in automobile insurance policies: a way to improve the bottom line of insurance companies |
title_full |
Risk factors selection in automobile insurance policies: a way to improve the bottom line of insurance companies |
title_fullStr |
Risk factors selection in automobile insurance policies: a way to improve the bottom line of insurance companies |
title_full_unstemmed |
Risk factors selection in automobile insurance policies: a way to improve the bottom line of insurance companies |
title_sort |
Risk factors selection in automobile insurance policies: a way to improve the bottom line of insurance companies |
author |
Segovia-Vargas, María-Jesús |
author_facet |
Segovia-Vargas, María-Jesús Camacho-Miñano, María-del-Mar Pascual-Ezama, David |
author_role |
author |
author2 |
Camacho-Miñano, María-del-Mar Pascual-Ezama, David |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Segovia-Vargas, María-Jesús Camacho-Miñano, María-del-Mar Pascual-Ezama, David |
dc.subject.por.fl_str_mv |
automobile insurance company risk factors bonus malus system rough set theory artificial intelligence. JEL C20 G22 companhia de seguros automobilísticos fatores de risco sistema de “bonus- malus” teoria de Rough Set inteligência artificial. |
topic |
automobile insurance company risk factors bonus malus system rough set theory artificial intelligence. JEL C20 G22 companhia de seguros automobilísticos fatores de risco sistema de “bonus- malus” teoria de Rough Set inteligência artificial. |
description |
Objective – The objective of this paper is to test the validity of using ‘bonus-malus’ (BM) levels to classify policyholders satisfactorily.Design/methodology/approach – In order to achieve the proposed objective and to show empirical evidence, an artificial intelligence method, Rough Set theory, has been employed.Findings – The empirical evidence shows that common risk factors employed by insurance companies are good explanatory variables for classifying car policyholders’ policies. In addition, the BM level variable slightly increases the explanatory power of the a priori risks factors.Practical implications – To increase the prediction capacity of BM level, psychological questionnaires could be used to measure policyholders’ hidden characteristics.Contributions – The main contribution is that the methodology used to carry out research, the Rough Set Theory, has not been applied to this problem. |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015-12-16 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Avaliado por pares |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://rbgn.fecap.br/RBGN/article/view/1741 10.7819/rbgn.v17i57.1741 |
url |
https://rbgn.fecap.br/RBGN/article/view/1741 |
identifier_str_mv |
10.7819/rbgn.v17i57.1741 |
dc.language.iso.fl_str_mv |
eng por |
language |
eng por |
dc.relation.none.fl_str_mv |
https://rbgn.fecap.br/RBGN/article/view/1741/pdf https://rbgn.fecap.br/RBGN/article/view/1741/pdf_1 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
FECAP |
publisher.none.fl_str_mv |
FECAP |
dc.source.none.fl_str_mv |
Review of Business Management; Vol. 17 No. 57 (2015); 1228-1245 RBGN Revista Brasileira de Gestão de Negócios; Vol. 17 Núm. 57 (2015); 1228-1245 RBGN - Revista Brasileira de Gestão de Negócios; v. 17 n. 57 (2015); 1228-1245 1983-0807 1806-4892 reponame:Revista Brasileira de Gestão de Negócios (Online) instname:Fundação Escola de Comércio Álvares Penteado (FECAP) instacron:FECAP |
instname_str |
Fundação Escola de Comércio Álvares Penteado (FECAP) |
instacron_str |
FECAP |
institution |
FECAP |
reponame_str |
Revista Brasileira de Gestão de Negócios (Online) |
collection |
Revista Brasileira de Gestão de Negócios (Online) |
repository.name.fl_str_mv |
Revista Brasileira de Gestão de Negócios (Online) - Fundação Escola de Comércio Álvares Penteado (FECAP) |
repository.mail.fl_str_mv |
||jmauricio@fecap.br |
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1798942368824557568 |